Diff-Retinex: Rethinking Low-light Image Enhancement with A Generative Diffusion Model

• URL: http://arxiv.org/abs/2308.13164v1
• Date: Fri, 25 Aug 2023 04:03:41 GMT
• Title: Diff-Retinex: Rethinking Low-light Image Enhancement with A Generative Diffusion Model
• Authors: Xunpeng Yi, Han Xu, Hao Zhang, Linfeng Tang, Jiayi Ma
• Abstract summary: We propose a physically explainable and generative diffusion model for low-light image enhancement, termed as Diff-Retinex. In the Retinex decomposition, we integrate the superiority of attention in Transformer to decompose the image into illumination and reflectance maps. Then, we design multi-path generative diffusion networks to reconstruct the normal-light Retinex probability distribution and solve the various degradations in these components respectively.
• Score: 28.762205397922294
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we rethink the low-light image enhancement task and propose a physically explainable and generative diffusion model for low-light image enhancement, termed as Diff-Retinex. We aim to integrate the advantages of the physical model and the generative network. Furthermore, we hope to supplement and even deduce the information missing in the low-light image through the generative network. Therefore, Diff-Retinex formulates the low-light image enhancement problem into Retinex decomposition and conditional image generation. In the Retinex decomposition, we integrate the superiority of attention in Transformer and meticulously design a Retinex Transformer decomposition network (TDN) to decompose the image into illumination and reflectance maps. Then, we design multi-path generative diffusion networks to reconstruct the normal-light Retinex probability distribution and solve the various degradations in these components respectively, including dark illumination, noise, color deviation, loss of scene contents, etc. Owing to generative diffusion model, Diff-Retinex puts the restoration of low-light subtle detail into practice. Extensive experiments conducted on real-world low-light datasets qualitatively and quantitatively demonstrate the effectiveness, superiority, and generalization of the proposed method.

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